Notwithstanding the usefulness of system dynamics in analyzing complex policy
problems, policy design is far from straightforward and in many instances
trial-and-error driven. To address this challenge, we propose to combine system
dynamics with network controllability, an emerging field in network science, to
facilitate the detection of effective leverage points in system dynamics models
and thus to support the design of influential policies. We illustrate our
approach by analyzing a classic system dynamics model: the World Dynamics
model. We show that it is enough to control only 53% of the variables to steer
the entire system to an arbitrary final state. We further rank all variables
according to their importance in controlling the system and we validate our
approach by showing that high ranked variables have a significantly larger
impact on the system behavior compared to low ranked variables
